Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data.
Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data.
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DOI：
10.1016/j.neuroimage.2010.05.063
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发表时间：
2010-10-15
期刊：
影响因子：
5.7
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Havlicek M;Jan J;Brazdil M;Calhoun VD
对理解大脑神经网络动态相互作用的兴趣日益增加，导致制定复杂的连通性分析方法。最近的研究应用基于标准多变量自回归（MAR）模型的格兰杰因果关系来评估大脑连接。然而，这种通常提出的方法的一个重要缺陷是，它要求分析的时间序列是平稳的，而由于功能磁共振成像（fMRI）时间序列的弱非平稳性质，这种假设大多被违反。因此，我们提出了一种在频域动态格兰杰因果关系的方法来评估功能mri数据中的功能网络连通性。与标准时不变MAR模型相比，结合前向和后向卡尔曼滤波器改善了估计，显著提高了动态方法的有效性和鲁棒性。在我们的方法中，首先通过独立分量分析（ICA）检测功能网络，ICA是一种将多元信号分离为最大程度独立分量的计算方法。然后用广义部分有向相干评价格兰杰因果关系的测度，该方法既适用于二元数据，也适用于多元数据。此外，该度量提供了频域因果关系的识别，这允许人们区分与实验范式相关的频率成分。通过模拟时间序列和两组在听觉感觉运动（SM）或听觉怪球辨别（AOD）任务中收集的fMRI数据，验证了动态MAR评估格兰杰因果关系的过程。最后，与标准时不变MAR模型的结果进行了比较。
Increasing interest in understanding dynamic interactions of brain neural networks leads to formulation of sophisticated connectivity analysis methods. Recent studies have applied Granger causality based on standard multivariate autoregressive (MAR) modeling to assess the brain connectivity. Nevertheless, one important flaw of this commonly proposed method is that it requires the analyzed time series to be stationary, whereas such assumption is mostly violated due to the weakly nonstationary nature of functional magnetic resonance imaging (fMRI) time series. Therefore, we propose an approach to dynamic Granger causality in the frequency domain for evaluating functional network connectivity in fMRI data. The effectiveness and robustness of the dynamic approach was significantly improved by combining a forward and backward Kalman filter that improved estimates compared to the standard time-invariant MAR modeling. In our method, the functional networks were first detected by independent component analysis (ICA), a computational method for separating a multivariate signal into maximally independent components. Then the measure of Granger causality was evaluated using generalized partial directed coherence that is suitable for bivariate as well as multivariate data. Moreover, this metric provides identification of causal relation in frequency domain, which allows one to distinguish the frequency components related to the experimental paradigm. The procedure of evaluating Granger causality via dynamic MAR was demonstrated on simulated time series as well as on two sets of group fMRI data collected during an auditory sensorimotor (SM) or auditory oddball discrimination (AOD) tasks. Finally, a comparison with the results obtained from a standard time-invariant MAR model was provided.
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